Structural basis of agonist specificity of α1A-adrenergic receptor

α1-adrenergic receptors (α1-ARs) play critical roles in the cardiovascular and nervous systems where they regulate blood pressure, cognition, and metabolism. However, the lack of specific agonists for all α1 subtypes has limited our understanding of the physiological roles of different α1-AR subtypes, and led to the stagnancy in agonist-based drug development for these receptors. Here we report cryo-EM structures of α1A-AR in complex with heterotrimeric G-proteins and either the endogenous common agonist epinephrine or the α1A-AR-specific synthetic agonist A61603. These structures provide molecular insights into the mechanisms underlying the discrimination between α1A-AR and α1B-AR by A61603. Guided by the structures and corresponding molecular dynamics simulations, we engineer α1A-AR mutants that are not responsive to A61603, and α1B-AR mutants that can be potently activated by A61603. Together, these findings advance our understanding of the agonist specificity for α1-ARs at the molecular level, opening the possibility of rational design of subtype-specific agonists.

The exact sample size (n) for each experimental group/condition, given as as a discrete number and unit of of measurement A statement on on whether measurements were taken from distinct samples or or whether the same sample was measured repeatedly The statistical test(s) used AND whether they are one-or or two-sided Only common tests should be described solely by name; describe more complex techniques in the Methods section.
A description of of all covariates tested A description of of any assumptions or or corrections, such as as tests of of normality and adjustment for multiple comparisons A full description of of the statistical parameters including central tendency (e.g. means) or or other basic estimates (e.g. regression coefficient) AND variation (e.g. standard deviation) or or associated estimates of of uncertainty (e.g. confidence intervals) For null hypothesis testing, the test statistic (e.g. F, t, r) with confidence intervals, effect sizes, degrees of of freedom and P value noted Give P values as exact values whenever suitable.

For Bayesian analysis, information on on the choice of of priors and Markov chain Monte Carlo settings
For hierarchical and complex designs, identification of of the appropriate level for tests and full reporting of of outcomes Estimates of of effect sizes (e.g. Cohen's d, Pearson's r), ), indicating how they were calculated Our web collection on statistics for biologists contains articles on many of the points above.

Software and code
Policy information about availability of of computer code Data collection

Data analysis
For manuscripts utilizing custom algorithms or or software that are central to to the research but not yet described in in published literature, software must be be made available to to editors and reviewers. We We strongly encourage code deposition in in a community repository (e.g. GitHub). See the Nature Portfolio guidelines for submitting code & software for further information.

Data
Policy information about availability of of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or or web links for publicly available datasets -A description of of any restrictions on on data availability -For clinical datasets or or third party data, please ensure that the statement adheres to to our policy No Sample size calculations were performed. For cryo-EM samples, eight grids of each sample were pre-screened to identify the optimal grid for data collection. The number of grids screened were random and was not limited by any experimental parameter. The sample size was deemed sufficient as it allowed to determine the structures.
No data were excluded from the analyses.
The Ca2+ assays were repeated three times, and the data are represented as mean ± SD of the three independent experiments. All attempts at replication were successful.
Division of particles into random halves is automatically performed during 3D reconstruction by Relion 4.0-beta and CryoSparc v3.3.1.
Blinding is not applicable for this study, as group allocation is not used.
Insect cell line Sf9 was obtained from Expression Systems. HEK293T cell lines were obtained from ATCC.
Authentication was not performed for this study.
All cell lines were tested negative for mycoplasma contamination.